# Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

arXiv: https://arxiv.org/abs/1906.01231 · HTML: https://mlsanity.com/paper/1906.01231/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021)
- [Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video Commenting](https://mlsanity.com/paper/2404.12782/) (2024)
- [Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection](https://mlsanity.com/paper/2102.06856/) (2021)
- [ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2203.02797/) (2022)
- [An entity-guided text summarization framework with relational heterogeneous graph neural network](https://mlsanity.com/paper/2302.03205/) (2023)
- [Towards Controlled and Diverse Generation of Article Comments](https://mlsanity.com/paper/2107.11781/) (2021)
- [You talk what you read: Understanding News Comment Behavior by Dispositional and Situational Attribution](https://mlsanity.com/paper/2308.02168/) (2023)

## Abstract (arXiv)

Automatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional encoder-decoder based models, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to understand the story. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.